无监督域调整用于基于超声波的HMI的会话间重新校准
Antonios Lykourinas1,2, Xavier Rottenberg2, Francky Catthoor2
1Department of Electrical and Computer Engineering, University of Patras, 26504 Patras, Greece.
Sensors (Basel, Switzerland)
|August 10, 2024
概括
这项研究探讨了基于超声波的人机接口的无监督域适应. 域-对手训练提高了准确性,但结果随着设置的变化而有所不同.
科学领域:
- 人与计算机的交互
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 人机界面 (HMI) 允许自然的用户机器交互,但需要经常重新校准.
- 动态环境导致数据漂移,导致HMI放弃,这是基于超声波 (美国) 的HMI中未得到充分探索的问题.
- 现有的重新校准方法通常需要标记数据,这对于持续适应是不切实际的.
研究的目的:
- 调查无监督域调整 (UDA) 算法,用于在一天内会话期间重新校准美国的HMI.
- 提出一种基于CNN的新架构,用于同时进行手腕旋转和手指手势预测.
- 在没有标记数据的情况下评估UDA在提高HMI性能方面的有效性.
主要方法:
- 开发了一个卷积神经网络 (CNN) 架构,用于预测手腕旋转角度和手指手势.
- 实施和评估各种无监督域调整 (UDA) 算法,包括域对抗训练 (DANN).
- 使用未标记的数据进行实验,以便在动态环境中进行重新校准.
主要成果:
- 拟议的CNN架构实现了最先进的性能,可训练参数减少了87.92%.
- 与没有重新校准相比,具有最佳初始化的域对抗训练 (DANN) 显示了平均24.99%的分类准确度增加.
- 来自UDA的性能提升取决于实验设置和UDA配置的一致性.
结论:
- 在动态环境中,UDA技术显示出适应美国HMI的前景,而不需要标记数据.
- 拟议的CNN架构为手势和旋转预测提供了一个高效的解决方案.
- 仔细考虑实验设置和UDA配置对于实现显著的性能增长至关重要.
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